System and methods for scoring telecommunications network data using regression classification techniques

ABSTRACT

Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features. The computing devices generate, based on the multi-class plural-factored elastic cluster model, a real-time demand-based forecast for the telecommunications services, and output the demand-based forecast.

BACKGROUND

Modern telecommunications networks offer a number of differentcommunications services, such as television services, data services, andtelephone services provided over both wired and wireless platforms.Multiple developing and legacy systems support and enable thesecommunication services. As technologies continue to develop,telecommunications providers must prioritize resource to meet increasingservice demands.

Telecommunication corporations deal with unique problems in obtaininghigh-resolution key geographical (spatial) and temporal (time demand)insights in product development, product pricing, and product marketingforecast. The lack of resolution can result in a loss of physical andmanual resources leading, for example, to unforeseen carbon footprintand wasted opportunities. Quantum computing infrastructure along withpowerful machine learning algorithms will address the lack of suchresolution with real-time processing of high volume data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary environment in which anembodiment of a demand forecasting service may be implemented;

FIG. 2 is a diagram illustrating an exemplary configuration that mayprovide an embodiment of the demand forecasting service;

FIG. 3 is a diagram illustrating communications in a portion of theMulti-class Plural-factored Elastic Clusters (MPEC) platform of FIG. 2;

FIGS. 4-6 are flow diagrams illustrating an MPEC compute algorithm forthe demand forecasting service;

FIGS. 7 and 8 are flow diagrams illustrating an MPEC convert algorithmfor the demand forecasting service;

FIG. 9 is an illustration of plot of coefficient slope vectors and datapoint clusters, according to an implementation described herein;

FIG. 10 is a schematic of a neural network for an MPEC process,according to an exemplary implementation;

FIG. 11 is a block diagram depicting exemplary components of a devicethat may correspond to one or more of the devices in FIGS. 1 and 2; and

FIG. 12 is an illustration of ideated fabric of an MPEC process,according to an exemplary implementation.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements. Also, the following detailed description does notlimit the invention.

Network service providers rely on increasing amounts and varieties ofdata to manage their networks. Network performance data, alarm data,alert data, event data, log data, live data, hardware data, Internet ofThings (IoT) data, and many other types of data may be collectedthroughout a heterogeneous network environment. Particularly, granulargeographic information enables strategic expansions, infrastructuredevelopment, and product development. Such granular geographicinformation is especially important in rolling out future wirelessproducts and services that require short range transmission equipment(e.g., 5G New Radio services). Additionally, telecommunications serviceproviders can benefit from the ability to harvest weather forecasts andevent data for timely development of products for emergency responses,such as natural calamities including wild fires, pandemics, andinfestations. Preparedness for such calamities requires accurate andgranular spatial and temporal analytics.

One barrier to obtaining such granular data analytics is the ability toquickly provide combinatorial evaluation of demand coefficient andproduct features tailored to customer needs. A successful demandforecasting service needs to provide efficient processing of individualdata components corresponding to (a) demand elasticity, (b) productfeatures, (c) geographic granularity corresponding to consumption andinfrastructure development feasibility, and (d) transaction data forcustomer segmentation, and then extract key factors from these inputdata components.

The demand forecasting service may combine transcending trends from (a)through (d) into segregated data formats. From the segregated data, thedemand forecasting service can derive multi-class elastic clusters ofcustomers, products, geographic, and/or time patterns using tailored anddeveloped machine learning algorithms. The demand forecasting servicetrains the algorithms with controlled neural networks and developshigh-resolution business-ready visualizations from granular data.

The technical data architecture and computing challenges to provide thedemand forecasting service include: data acquisition and storage (e.g.,with multiple data sources); data processing (e.g., processingpipeline); integrating machine learning with deep learning on acontinuous basis for live feeds for real time visualizations (e.g., realtime data feeds); and training the networks from repeated simulations ofpreviously trained algorithms.

Systems and methods described herein include a demand forecastingservice that provides granular information for differenttelecommunications demand forecasts in real-time. The systems andmethods take into account factors such as weather conditions,catastrophic events, hardware, geography, marketing, sales, core logic,social media, raw materials forecast, etc., and prioritize the differentfactors in relation to product services and forecasting. According to animplementation, the systems and methods provide a unique infrastructureto apply a multi-class cluster algorithm, which may be referred to hereas a Multi-Class Plural-Factored Elastic Clusters (MPEC) analysis, inquantum pipelines. The systems and methods provide an institutionalquantum Artificial Intelligence (AI) infrastructure development in acloud environment. The system and methods provide telecommunicationproduct geographic demand forecasts, telecommunication product temporaldemand forecasts, telecommunication service deployment forecasts, andtelecommunication pricing refinement in both feature and demanddimensions. The system and methods further provideweather-forecast-integrated elastic-cluster-based analytics for (a)emergency response preparedness, (b) institutional marketing forecasts,(c) institutional pricing forecasts, and (d) institutional salesforecasts.

Systems and methods described herein develop optimized, real-time andsecure forecast methods from mainstream and social media sources. Whilethe systems and methods described herein are discussed in the context offorecasting for telecommunication products and service development,other applications may be directed toward other industries, such asretail. Also, while certain components may be described with referenceto the term MPEC, other analytical tools could be used to performfeatures described herein.

FIG. 1 is a diagram of an exemplary environment 100 in which the systemsand/or methods, described herein, may be implemented. As shown in FIG.1, environment 100 may include end devices 110-1 to 110-X (referred toherein collectively as “end devices 110” and individually as “end device110”), an access network 120, a core network 130, a backhaul network140, a data network 150, and a service network 160. According to animplementation, access network 120, core network 130, backhaul network140, and service network 160 may be referred to as a “traffic network,”which may be owned/operated by the same service provider. According toanother implementation, one or more data networks 150 may also beincluded in the traffic network.

End device 110 includes a device that has computation and communicationcapabilities. End device 110 may be implemented as a mobile device, aportable device, or a stationary device. By way of further example, enddevice 110 may be implemented as a smartphone, a personal digitalassistant, a tablet, a wearable device, a set top box, an infotainmentsystem in a vehicle, a smart television, a gaming system, a musicplaying system, or some other type of user device. End device 110 may beimplemented as a Machine Type Communication (MTC) device, an Internet ofThings (IoT) device, a user device, or some other type of end node.According to various exemplary embodiments, end device 110 may beconfigured to execute various types of software (e.g., applications,programs, etc.). The number and the types of software may vary from oneend device 110 to another end device 110.

Access network 120 includes one or multiple networks of one or multipletypes. For example, access network 120 may be implemented to include aterrestrial network. According to an exemplary implementation, accessnetwork 120 includes a radio access network (RAN). For example, the RANmay be a Fourth Generation (4G) RAN, a Fifth Generation (5G) RAN, or afuture generation RAN. By way of further example, access network 120 mayinclude an Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) of aLong Term Evolution (LTE) network or an LTE-Advanced (LTE-A) network.Access network 120 may also include other types of networks, such as aWiFi network, a local area network (LAN), a personal area network (PAN),or other type of network that provides access to or can be used as anon-ramp or interface to core network 130 or backhaul network 140.Depending on the implementation, access network 120 may include varioustypes of network devices and wireless stations 125. For example,wireless station 125 of access network 120 may include a base station(BS), a base transceiver station (BTS), a Node B, an evolved Node B(eNB), a next generation Node B (gNB), a remote radio head (RRH), an RRHand a baseband unit (BBU), a BBU, and/or other type of node (e.g.,wireless, wired, optical) that includes network communicationcapabilities.

Core network 130 may manage communication sessions for end devices 110.For example, core network 130 may establish an Internet Protocol (IP)connection between end device 110 and a particular data network 150.Furthermore, core network 130 may enable end device 110 to communicatewith an application server, and/or another type of device, located in aparticular data network 150 using a communication method that does notrequire the establishment of an IP connection between end device 110 anddata network 150, such as, for example, Data over Non-Access Stratum(DoNAS). Depending on the implementation, core network 130 may includevarious network devices 135, such as a gateway, a support node, aserving node, a mobility management entity (MME), Access and MobilityFunction (AMF), as well other network devices pertaining to variousnetwork-related functions, such as billing, security, authentication andauthorization, network polices, subscriber profiles, and/or othernetwork devices that facilitate the operation of core network 130.

Backhaul network 140 includes one or multiple networks of one ormultiple types and technologies. According to an exemplaryimplementation, backhaul network 140 includes a backbone network. Forexample, the backbone network may be implemented as an optical transportnetwork, an ultra-high capacity wireless backhaul network, an Ethernetbackhaul network, a dark fiber network, or another suitable architecture(e.g., Internet Protocol (IP)/Multiprotocol Label Switching (MPLS),millimeter wave technology, etc.). Depending on the implementation,backhaul network 140 may include switches, routers, repeaters, varioustypes of optical network elements (e.g., multiplexers, de-multiplexers,switches, transmitters, receivers, etc.), and/or other types of networkdevices. For purposes of illustration and description, network devices145 may include various types of network devices that may be resident inbackhaul network 140, as described herein. Backhaul network 140 may alsoinclude a fronthaul network.

Data network 150 may include a packet data network. A particular datanetwork 150 may include, and/or be connected to and enable communicationwith, a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), an optical network, a cable televisionnetwork, a satellite network, a wireless network (e.g., a code divisionmultiple access network, a general packet radio service (GPRS) network,and/or an LTE network), an ad hoc network, a telephone network (e.g.,the Public Switched Telephone Network (PSTN) or a cellular network), anintranet, or a combination of networks. Some or all of a particular datanetwork 150 may be managed by a communication services provider thatalso manages backhaul network 140, core network 130, radio accessnetwork 120, and/or particular end devices 110. For example, in someimplementations, a particular data network 150 may include an IPMultimedia Sub-system (IMS) network (not shown in FIG. 1). An IMSnetwork may include a network for delivering IP multimedia services andmay provide media flows between two different end devices 110, and/orbetween a particular end device 110 and external IP networks or externalcircuit-switched networks (not shown in FIG. 1). Data network 150 mayinclude various types of network devices 155, which may implementdifferent network functions described further herein.

Service network 160 may include one or multiple networks of one ormultiple types. For example, service network 160 may include a cloudnetwork, a WAN, a MAN, a service provider network, a private IP network,some other type of backend network, and so forth. As illustrated,according to an exemplary embodiment, service network 160 includesnetwork devices 165 and a MPEC platform 170.

Service network 160 may include various types of network devices 165,which may implement different network functions described furtherherein. For example, network devices 165 may provide various physicalresources (e.g., processors, memory, storage, communication interface,etc.), software resources (e.g., operating system, etc.) andvirtualization elements (e.g., hypervisor, container engine, etc.).According to other exemplary embodiments, MPEC platform 170 or portionsthereof may be combined in a single device or a distributed environment.In another implementation, service network 160 may be included as partof another network, such core network 130, backhaul network 140, or datanetwork 150.

MPEC platform 170 may include network devices for providing real-timeforecasting geographic demand, such as for telecommunications servicesacross the multiple networks in the traffic network of networkenvironment 100 (e.g., devices in access network 120, core network 130,backhaul network 140, data network 150, and/or service network 160). Asdescribed further herein, MPEC platform 170 may implement a dataingestion process and a MPEC compute pipeline to generate geographicdemand forecasts, telecommunication product temporal demand forecasts,telecommunication service deployment forecasts, and telecommunicationpricing refinement in both feature and demand dimensions.

MPEC platform 170 and/or other network devices described herein mayrefer to a dedicated hardware component implementing a network functioninstance or to a hardware component that is part of a common sharedphysical infrastructure used to implement virtualized network functioninstances using software defined networking (SDN) or another type ofvirtualization technique. Thus, MPEC platform 170 may be configured toimplement a particular network function instance as a Virtual NetworkFunction (VNF) (e.g., in a virtual machine), as a Cloud-native NetworkFunction (CNF) (e.g., in a container), as a serverless architectureevent handler, and/or using a different type of virtualization. Thecommon shared physical infrastructure may be implemented using one ormore computer devices in a cloud computing center, a multi-access edgecomputing (MEC) system associated with a wireless station, and/or inanother type of computer system.

The number and arrangement of devices in environment 100 are exemplary.According to other embodiments, environment 100 may include additionaldevices and/or differently arranged devices, than those illustrated inFIG. 1. The number and arrangement of networks in environment 100 areexemplary. According to other embodiments, environment 100 may includeadditional networks, fewer networks, and/or differently arrangednetworks than those illustrated in FIG. 1.

A network device, as described herein, may be implemented according to acentralized computing architecture, a distributed computingarchitecture, or a cloud computing architecture (e.g., an elastic cloud,a private cloud, a public cloud, etc.). Additionally, a network devicemay be implemented according to one or multiple network architectures(e.g., a client device, a server device, a peer device, a proxy device,and/or a cloud device).

Environment 100 includes links between the networks and between thedevices. Environment 100 may be implemented to include wired, optical,and/or wireless links among the devices and the networks illustrated. Acommunicative connection via a link may be direct or indirect. Forexample, an indirect communicative connection may involve anintermediary device and/or an intermediary network not illustrated inFIG. 1. Additionally, the number and the arrangement of linksillustrated in environment 100 are exemplary.

FIG. 2 is a diagram illustrating an exemplary implementation of a MPECplatform 170 configuration in which MPEC platform 170 may becommunicatively coupled to data sources of network environment 100. Asshown in FIG. 2, an environment 200, which is consistent with thetraffic network of network environment 100, may include multiple datasources 205, data ingestion system 210, a MPEC compute pipeline 220, acustomer model depositions 230, and visualization user interface (UI)240. MPEC compute pipeline 220, a customer model depositions 230, and UIvisualizations may be implemented, for example, in MPEC platform 170.MPEC pipelines as depicted in FIG. 2 enable the infrastructure forhybrid quantum computing and distribution of models built in theinfrastructure.

Data sources 205 may include a computing device or network device thatcollects data to support network analytics and/or real-time networkmanagement. Data sources 205 may correspond, for example, to wirelessstations 125 and/or network devices 135/145. In other implementations,data sources 205 may additionally correspond to network devices 155and/or 165. Data generated by data sources 205 may include real-timedata (e.g., streamed), batch data (e.g., periodically reported), anddeposited (e.g., fixed or occasionally reported data). Examples ofreal-time data generated by data sources 205 may include, for example,weather data, IoT data, and social media data. An example of batch datagenerated by data sources 205 may include, for example, transactionaldata. Examples of deposited data generated by data sources 205 mayinclude, for example, firmographics (e.g., firm demographics),demographics, real estate, language processed, epidemics, and sensordata. In another implementation, data generated by data sources 205 mayinclude social media data, external internet scraping data,purchase-related data, etc. Data sources 205 may be configured toprovide data to a data ingestion system 210.

Data ingestion system 210 may include logic that provides an ingestionservice that collects, ingests, stores, and manages various types ofdata in support of the MPEC platform 170. According to animplementation, data ingestion system 210 may include a segregation unit212 and an extract/process unit 214. Data ingestion system 210 may alsoinclude a mass storage device, such as an elastic storage server.

Segregation unit 212 may include logic that performs segregation of datafrom data sources 205, such as segregation based on spatial factors(e.g., geographic, distance, coverage areas, etc.) and temporal factors(e.g., time, periodicity, etc.).

Extract/process unit 214 may include logic to identify elasticityfactors in data from data sources 205. In one implementation, elasticityfactors may, for example, be configured by a user as part of ananalytics request. In another implementation, elasticity factors may bedefined for MPEC platform 170. Extract/process unit 214 may identifyparticular fields and/or formats in incoming data (from data source 205)with elasticity factors.

Data ingestion system 210 may additionally include logic that performsother storage-related and/or data management-related functions, such as,formatting data (e.g., transforming raw data into a particular format,etc.), compression and decompression, data integrity verification,adding data, deleting data, updating data, maintaining data quality,providing data access, extraction, encryption, classification of data,etc., for MPEC compute pipeline 220, a database, or another type of datastructure.

MPEC compute pipeline 220 may include logic to perform master convertand master compute algorithms. According to an implementation, MPECcompute pipeline 220 may include a master convert unit 222, a mastercompute unit 224, and a cost reduction and refinement module 226.

Master convert unit 222 may include logic to perform master convertalgorithms and sub algorithms. According to an implementation, masterconvert unit 222 may be implemented in a separate processing core frommaster compute unit 224. The MPEC convert algorithm facilitates themechanics of either exclusive (classic or quantum) or inclusive (hybridand classic) computing. The MPEC master convert algorithm enablesdecision making for conversion of data. Parameters, such as data typeand data size, may be used as input parameters for decision making.Master convert unit 222 may convert data from classic bits to quantumbits with an estimate of computation cost, efficiency and urgency ofanalytics provision in both classical and quantum computing environment.In the case of output of data amenable for quick classic computing, thedata is processed in a classic pipeline, as described in connection withFIG. 6. As described in connection with FIG. 7, in the case of output ofquantum demanding data, data will be converted to use specific convertprocesses. Specific gates, operators etc., are used for specific datatypes in the conversion algorithm.

Master compute unit 224 may include logic to perform master computealgorithms and sub algorithms. As described further in connection withFIGS. 4-6, the master compute process may first generate vectorscomprised of slopes from plural coefficients to determine demandelasticity from plural features. The vectors can be used to generatehypothetical barriers for plural clusters comprised of characteristicproperties, resulting in multi-dimensional space for ultimatelyobtaining class bins for products and customers. The master computeprocess will engage several regressional and classification/segmentationalgorithms as needed.

According to an implementation, the MPEC master compute algorithm (andthe sub master compute algorithms) uses massive computing resources thatrun in real time. The computing loads may have high demands facilitatedby distributed computing, parallel computing, and/or quantum computingor alternatively hybrid computing. The master compute may generatemultiple multi-class bins, such as bins for geographic demand ofproducts, time demand of products, geographic and/or time demand withdiffering product features, and weather forecast predicted preparednessinformatics for emergency response based product development. Forexample, according to an implementation, the batch data or streamed dataincludes weather forecast data that is ingested into elastic clusters toprovide emergency-related telecommunications predictions for naturaldisasters, such as hurricanes, floods, winter storms, etc.

Cost reduction and refinement module 226 may include logic to performsub-master compute tasks. As described further in connection with FIGS.4 and 5, the sub-master compute processes include supporting algorithmssuch as cost reduction, gradient boosts, and neural network algorithms.

Customer model depositions 230 may store analysis results responsive to,for example, a customer analytics request. Customer model depositions230 may be implemented, for example, as an elastic storage server and/orcloud object storage.

Visualization UI 240 may provide high-resolution business-readyvisualizations from results in customer model depositions 230.Visualization UI 240 may include an application accessed via end device110, for example. Visualization UI 240 may provide results of the demandforecasting service, such as graphical displays, charts, maps, etc.,that show the results from customer model depositions 230. According toan implementation, visualization UI 240 may receive dedicatedapplication programming interface (API) calls from an applicationexecuted on end device 110. According to another implementation,visualization UI 240 may be a web-based interface accessed via a webbrowser.

FIG. 3 is a diagram illustrating typical communications in a portion 300of MPEC platform 170 for generating granular information for differenttelecommunications demand forecasts, according to an implementation.Data (e.g., from data sources 205) may be received into initial storage305 or quick ingest 315. Initial storage 305 may receive, for example,deposited data from data sources 205, which may be stored in cloudobject storage. Data from initial storage 305 may be ingested and storedin a separate cloud infrastructure 310. Quick ingest 315 may include anelastic storage component and perform real-time analytics for streamingdata.

Data from initial storage 305 and/or quick ingest 315 may be fed into anextract, transform, and load (ETL) 320 processes that uses an elasticstorage server to move data into and out of storage. ETL process 320 maytransform segregated data into processed data that may be stored inprocessed/final storage 325 and simultaneously used to build an initialMPEC model 330. Processed/final storage 325 may include, for example, anelastic storage server, a cloud object storage, and/or a data warehouse.

MPEC build 330 may ingest data from ETL processes 320 to build anartificial intelligence model. For example, MPEC build 330 may useAPACHE SPARK with DATALAKE AI to assemble an MPEC model. At qubitconvert 335, if necessary, data may be converted from classic bits toqubits and quantum data optimization may be performed at quantum AI 340.MPEC compute 345 may apply recursive neural networks to associate pluralelastic inputs and multi-class cluster inputs with output class bins.Model refinement 350 may perform cost reductions and/or other modelrefinements, and insight deposition data 355 may be stored in elasticstorage and/or cloud object storage.

Using, for example, end device 110, a user may access insight depositiondata 355 to provide validation and/or feedback at validation/feedback360. Data from insight deposition data 355 may be presented as real-timeforecasts for users (e.g., via end device 110) at forecast models 365.

FIGS. 4-6 are flow diagrams illustrating a process 400 for performingmulti-class plural-factored elastic clustering. Process 400 may beperformed, for example, by MPEC compute pipeline 220. Some portions ofprocess 400 are discussed below in the context of graph 900 of FIG. 9,which illustrates concepts described herein.

Referring to FIG. 4, process 400 may include computing a plurality ofelasticity coefficients (block 410), and obtaining multiple elasticityslope vectors (block 420). For example, MPEC compute pipeline 220 mayreceive preprocessed data from data ingestion system 210 and compute aplurality of elasticity coefficients, such as a price elasticity ofdemand or price elasticity of supply. The plurality of elasticitycoefficients may reflect, for example, the degree to which the effectivedemand for a product or service changes as the respective price orsupply changes. A typical analysis may use one coefficient to forecastone particular factor. In contrast, according to implementationsdescribed herein, multiple coefficients are used to derive a slopevector for a two-dimensional X-Y plane, where regression analysis isapplied to determine a slope for plotted points of each customerattribute. The customer attributes in some cases are complex, comprisingcomposites of multiple attributes. Multi-plurality of the attributesresults in a continuum of archetypes. Typical clustering andclassification algorithms fail to provide granularity in a continuum.MPEC enables segmentation of complex archetypes into distinct quadrantsin a two-dimensional X-Y plane. For example, as illustrated in FIG. 9,multiple slopes 905-1 through 905-n may be calculated for differentproducts and attributes that identify the elasticity in multiplecharacteristic dimensions.

Process 400 may also include computing plural clusters from distancediscriminant analysis (block 430), and embedding multi-class clustersinto the elasticity slope vectors (block 440). For example, usingclustering algorithms, such as K-means clustering, random forestclassification, etc., MPEC compute pipeline 220 may derive clusters ofdata points, such as clusters 910-1 through 910-n of FIG. 9. In otherimplementations, MPEC compute pipeline 220 may apply Pareto analysis,Density-Based Spatial Clustering of Applications with Noise (DBSCAN),Expectation minimization (EM), or agglomerative hierarchical clusteringto compute the plural clusters. These clusters 910 may be added on tothe coefficient slope vectors 905 and placed accordingly. In otherwords, the slope vectors 905 may be used as heightened planes and ordersto separate the multiple clusters. The clusters represent distinctstatistical limits and properties for multi-dimensional profiling.According to an implementation, linear distance measurements may be usedto discriminate how each cluster 910 is separated within the space ofeach slope vector 905. Thus, high granularity may be obtained in acollection of multiple features.

Process 400 may further include computing specific class bins in K-means(block 450), and assign specific class bins (block 460). For example,MPEC compute pipeline 220 may identify distinct bins for differentclassifications, such as different bins of customers, different bins ofprices, different bins of geographical areas, or different bins ofcomposites. Bins may be differentiated, for example, with specificnumerical gradients and/or thresholds. The binning of clusters may beperformed using, for example, K-means algorithms, Pareto analysis. Theapproach enables clustering on linearity functions derived from complexnon-linear data. Clusters may be assigned to different bins based onstatistical limits.

Process 400 may include sub-processes 500 and 600 of FIGS. 5 and 6,respectively. Sub-process 500 may be executed, for example, to refineclusters for process blocks 430 and 440. Process 500 may includeidentifying plural features from non-linear data (block 510), derivingindividual clusters (block 520), computing a cluster distance matrix(block 530), and determining the number of clusters meets a selectedcriteria based on the distance matrix using elbow methods (block 540).For example, MPEC compute pipeline 220 may detect groups of data points,composites, archetypes, etc., associated with a customer, product orservice and derive an initial set of individual clusters (e.g., clusters910). MPEC compute pipeline 220 may compute a cluster distance matrixfor each cluster based on a distance from slope vectors 910 anddetermine if the number of clusters is appropriate. For example, MPECcompute pipeline 220 may use an elbow method to determine an optimalnumber of clusters.

Sub-process 500 may also include merging clusters, if there are too manyclusters (block 550), and updating the matrix distance for the mergedcluster (block 560). For example, if MPEC compute pipeline 220determines that the number of clusters is too large adjacent clustersmay be merged until an acceptable number of clusters is achieved.

Sub-process 600 may be executed, for example, to use a convolutionalneural network (CNN) or artificial neural network (ANN). Sub-process 600may be executed, for example, to refine and optimize models for processblocks 430 through 460. Sub-process 600 may include selecting a pluralfeatures map (block 610), performing weight initialization (block 620),entering a pattern network (block 630), determining a winning neuron(block 640), updating the assigned weight (block 650), conducting costfunction reduction (block 660), and confirming validating criteria ismet (670). For example, as illustrated in FIG. 10, MPEC compute pipeline220 may perform cost reduction and model refinement using a mode similarto FIG. 10, where weights (e.g., W1, W2) between nodes are optimizedaccordingly to provide a least-cost path to output class bins. Bins maybe defined based on the weighted average thresholds for each bin.

FIGS. 7 and 8 are flow diagrams illustrating a process 700 forperforming decision making for conversion of data from classic bits toquantum bits (or qubits). Process 700 may be performed, for example, byMPEC compute pipeline 220. Process 700 may correspond, for example, toblocks 330-345 of FIG. 3.

Referring to FIG. 7, process 700 may include receiving an API analyticrequest (block 710), and determining if the data load can be timelymanaged using classic compute bits (block 720). For example, an analyticrequest from a customer (e.g., using end device 110) may defineparticular factors or output to predict demand for a network product orservice. The analytic request may be formatted (e.g., using an API) intoa format that can be ingested, for example, at MPEC build 330. Based onthe number of factors, the type of data, the data volume, etc., MPECcompute pipeline 220 may determine if the data load is more efficientwith classic computing bits or qubits. For example, using the time stampon a data storage system, thresholds could be used to define anddetermine a data load. For a load exceeding a classical computingthreshold, the data can be directed to quantum processes.

If the data load can be more timely managed using classic compute bits(block 720—Yes), process 700 may include processing the analytic requestusing classic cloud computing (block 730). For example, MPEC computepipeline 220 may apply data to an MPEC compute process (e.g., process400) using classic bits, such as shown at MPEC compute 345 of FIG. 3.

If the data load can be more timely managed using quantum computing(block 720—No), process 700 may include performing qubit conversion(block 740). Process block 740 may correspond to qubit convert block 335of FIG. 3. The qubit convert may use multiple approaches to createquantum states to convert classical data. For example, compression in aclassic environment, decompression in a quantum environment enhances bitto qubit conversion performance even more than direct conversion.However, the decision for the use of this compression and decompressiondepends on the type of data. Independent and equiprobable data would notneed compression, while asymmetric and predicted patterns benefit withcompression and decompression. Principles from nuclear magneticresonance spectroscopy that are used to detect proton-protoninteractions using pulse sequences may be applied to the quantum gatesequences for optimization of quantum operations fine-tuned for MPEC.For example, depending on the number of factors and the type of datarequired for an analytic request, data matrices will be routed throughquantum state preparation procedures. Process block 740 is describedfurther in connection with FIG. 8.

As illustrated in FIG. 8, process block 740 may include receivingclassic network data input (block 805) and performing statistical datatypecasting (block 810). For example, the quantum state preparation mayinclude data typecasting that may be done by detecting patterns andsymmetry in the data. In the event of detected symmetry (block810—“symmetry and patterns”), adaptive probability disseminationprotocols may be used (block 815), and an optimal/symmetric compressionalgorithm may be used in classic computing environment (block 820). Inthe event of lack of symmetry (block 810—“independent,equiprobability”), all data may be treated uniformly with genericcompression algorithms in classic environment (block 825).

After performing the generic classic compression or symmetriccompression, data is fed into a quantum channel (block 830). The quantumchannel of MPEC pipeline 220 may include a computing device thatoperates on the principles of quantum mechanics capable of mappingclassical bits into qubits. The quantum channel may perform quantum datadecompression (block 835). Compression in classic environments anddecompression in quantum environments respectively and collectivelyenhance overall computing performance in comparison to classicalcircuitry.

MPEC compute pipeline 220 may use variational gate optimization formulti-qubit operations. For example, the quantum channel may performquantum state separation (block 835) to sort the quantum data basedreusability (e.g., erasure or no erasure). For erasure (block840—“erasure”), data may be routed to an erasure quantum circuit family(block 845) and a gate depth may be determined (block 850). For noerasure (block 840—“no erasure”), data may be routed to a no erasurequantum circuit family (block 855) and a gate depth may be determined(block 860). Based on the determined gate depth, MPEC compute pipeline220 may perform qubit conversion based on the selected gate (block 865).

Returning to FIG. 7, after a qubit conversion is performed, process 700may include performing quantum compute (block 745) in the cloudinfrastructure or in an in-house quantum channel. Process block 745 maycorrespond to quantum AI block 340 of FIG. 3. For example, MPEC computepipeline 220 may apply quantum data to an MPEC compute process usingqubits to assign bins in near real time or in real-time streaming withcomputationally heavy machine learning.

FIG. 11 is a block diagram depicting exemplary components of a device1100 that may correspond to end device 110, wireless station 125, or oneof network devices 135/145/155/165, or devices in MPEC platform 170(including quantum channels). End device 110, wireless station 125,network devices 135/145/155/165, or MPEC platform 170 may include one ormore devices 1100. Device 1100 may include a bus 1110, a processor 1120,a memory 1130, mass storage 1140, an input device 1150, an output device1160, and a communication interface 1170.

Bus 1110 includes a path that permits communication among the componentsof device 1100. Processor 1120 may include any type of single-coreprocessor, multi-core processor, microprocessor, latch-based processor,and/or processing logic (or families of processors, microprocessors,and/or processing logics) that interprets and executes instructions. Inother embodiments, processor 1120 may include an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA),and/or another type of integrated circuit or processing logic. Forexample, the processor 1120 may be an x86 based CPU, and may use anyoperating system, which may include varieties of the Windows, UNIX,and/or Linux. The processor 1120 may also use high-level analysissoftware packages and/or custom software written in any programmingand/or scripting languages for interacting with other network entitiesand providing applications to, for example, a plurality of datareporting device 205 which are communicatively coupled to servicenetwork160.

Memory 1130 may include any type of dynamic storage device that maystore information and/or instructions, for execution by processor 1120,and/or any type of non-volatile storage device that may storeinformation for use by processor 1120. For example, memory 1130 mayinclude a RAM or another type of dynamic storage device, a ROM device oranother type of static storage device, and/or a removable form ofmemory, such as a flash memory. Mass storage device 1140 may include anytype of on-board device suitable for storing large amounts of data, andmay include one or more hard drives, solid state drives, and/or varioustypes of redundant array of independent disks (RAID) arrays. Massstorage device 1140 may be suitable for storing data associated withdata sources 205 for distributing uniform format messages to, forexample, MPEC platform 170.

Input device 1150, which may be optional, can allow an operator to inputinformation into device 1100, if required. Input device 1150 mayinclude, for example, a keyboard, a mouse, a pen, a microphone, a remotecontrol, an audio capture device, an image and/or video capture device,a touch-screen display, and/or another type of input device. In someembodiments, device 1100 may be managed remotely and may not includeinput device 1150. Output device 1160 may output information to anoperator of device 1100. Output device 1160 may include a display, aprinter, a speaker, and/or another type of output device. In someembodiments, device 1100 may be managed remotely and may not includeoutput device 1160.

Communication interface 1170 may include a transceiver that enablesdevice 1100 to communicate over communication links with other devicesand/or systems. Communications interface 1170 may be a wirelesscommunications (e.g., radio frequency (RF), infrared, and/or visualoptics, etc.), wired communications (e.g., conductive wire, twisted paircable, coaxial cable, transmission line, fiber optic cable, and/orwaveguide, etc.), or a combination of wireless and wired communications.Communication interface 1170 may include a transmitter that convertsbaseband signals to RF signals and/or a receiver that converts RFsignals to baseband signals. Communication interface 1170 may be coupledto one or more antennas for transmitting and receiving RF signals.Communication interface 1170 may include a logical component thatincludes input and/or output ports, input and/or output systems, and/orother input and output components that facilitate thetransmission/reception of data to/from other devices. For example,communication interface 1170 may include a network interface card (e.g.,Ethernet card) for wired communications and/or a wireless networkinterface (e.g., a Wi-Fi) card for wireless communications.Communication interface 1170 may also include a universal serial bus(USB) port for communications over a cable, a Bluetooth wirelessinterface, a radio-frequency identification (RFID) interface, anear-field communication (NFC) wireless interface, and/or any other typeof interface that converts data from one form to another form.

As described below, device 1100 may perform certain operations relatingto the unified collection service. Device 1100 may perform theseoperations in response to processor 1120 executing software instructionscontained in a computer-readable medium, such as memory 1130 and/or massstorage 1140. The software instructions may be read into memory 1130from another computer-readable medium or from another device. Thesoftware instructions contained in memory 1130 may cause processor 1120to perform processes described herein. Alternatively, hardwiredcircuitry may be used in place of, or in combination with, softwareinstructions to implement processes described herein. Thus,implementations described herein are not limited to any specificcombination of hardware circuitry and software.

Although FIG. 11 shows exemplary components of device 1100, in otherimplementations, device 1100 may include fewer components, differentcomponents, additional components, or differently-arranged componentsthan depicted in FIG. 11.

FIG. 12 is an illustration of concepts of an MPEC process for aparticular use case. In the example of FIG. 12, the fluidity ofintegrating the elasticity coefficients with a plurality of product andcustomer features in a neural network is depicted. Elasticity slopesconstitute the threads in which the clusters are embedded in the MPEC.MPEC compute pipeline 220 may receive preprocessed data from dataingestion system 210 and compute a plurality of elasticity coefficients.A plurality of product features includes pricing history, transactionanalysis, geographical features, characteristic features, andcompetitive features. A plurality of customer features includes businesssector, revenue, regional information, market capture, time stamps,size, and profitability.

Systems and methods described herein provide demand forecasting servicefor telecommunications services in a network and network optimizationwith network data typecasting. The systems and methods use classical andquantum computing devices. The computing devices evaluate data typesusing statistical symmetry recognition and operate between classical andquantum environments. In one implementation, computing devices receivedeposited data, batch data, and streamed data that relates totelecommunications services and segregate the data into spatial andtemporal factors. The computing devices receive an analytic request fora forecast of the telecommunications services and conduct a multi-classplural-factored elastic cluster (MPEC) analysis for thetelecommunications services using the segregated data distilled in dataprocessing pipelines. The MPEC analysis includes generating vectorscomprised of slopes from plural coefficients to determine demandelasticity from plural features. The computing devices generate, basedon the multi-class plural-factored elastic cluster model, a real-timedemand-based forecast for the telecommunications services, and networkdemand present the demand-based forecast to a user/network optimizationengineer.

As set forth in this description and illustrated by the drawings,reference is made to “an exemplary embodiment,” “an embodiment,”“embodiments,” etc., which may include a particular feature, structureor characteristic in connection with an embodiment(s). However, the useof the phrase or term “an embodiment,” “embodiments,” etc., in variousplaces in the specification does not necessarily refer to allembodiments described, nor does it necessarily refer to the sameembodiment, nor are separate or alternative embodiments necessarilymutually exclusive of other embodiment(s). The same applies to the term“implementation,” “implementations,” etc.

The foregoing description of embodiments provides illustration, but isnot intended to be exhaustive or to limit the embodiments to the preciseform disclosed. Thus, various modifications and changes may be madethereto, and additional embodiments may be implemented, withoutdeparting from the broader scope of the invention as set forth in theclaims that follow. The description and drawings are accordingly to beregarded as illustrative rather than restrictive.

The terms “a,” “an,” and “the” are intended to be interpreted to includeone or more items. Further, the phrase “based on” is intended to beinterpreted as “based, at least in part, on,” unless explicitly statedotherwise. The term “and/or” is intended to be interpreted to includeany and all combinations of one or more of the associated items. Theword “exemplary” is used herein to mean “serving as an example.” Anyembodiment or implementation described as “exemplary” is not necessarilyto be construed as preferred or advantageous over other embodiments orimplementations.

In addition, while series of blocks have been described with regard tothe processes illustrated in FIGS. 3-7, the order of the blocks may bemodified according to other embodiments. Further, non-dependent blocksmay be performed in parallel. Additionally, other processes described inthis description may be modified and/or non-dependent operations may beperformed in parallel.

Embodiments described herein may be implemented in many different formsof software executed by hardware. For example, a process or a functionmay be implemented as “logic,” a “component,” or an “element.” Thelogic, the component, or the element, may include, for example, hardware(e.g., processor 1120, etc.), or a combination of hardware and software.

Embodiments have been described without reference to the specificsoftware code because the software code can be designed to implement theembodiments based on the description herein and commercially availablesoftware design environments and/or languages. For example, varioustypes of programming languages including, for example, a compiledlanguage, an interpreted language, a declarative language, or aprocedural language may be implemented.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another, thetemporal order in which acts of a method are performed, the temporalorder in which instructions executed by a device are performed, etc.,but are used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term) to distinguish the claim elements.

Additionally, embodiments described herein may be implemented as anon-transitory computer-readable storage medium that stores data and/orinformation, such as instructions, program code, a data structure, aprogram module, an application, a script, or other known or conventionalform suitable for use in a computing environment. The program code,instructions, application, etc., is readable and executable by aprocessor (e.g., processor 1120) of a device. A non-transitory storagemedium includes one or more of the storage mediums described in relationto memory 1130.

To the extent the aforementioned embodiments collect, store or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage and use of such information may besubject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as may be appropriatefor the situation and type of information. Storage and use of personalinformation may be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

No element, act, or instruction set forth in this description should beconstrued as critical or essential to the embodiments described hereinunless explicitly indicated as such. All structural and functionalequivalents to the elements of the various aspects set forth in thisdisclosure that are known or later come to be known are expresslyincorporated herein by reference and are intended to be encompassed bythe claims.

What is claimed is:
 1. A method comprising: receiving, by one or morecomputing devices in a network, deposited data, batch data, and streameddata that relates to telecommunications services and telecommunicationnetwork components; segregating, by the one or more computing devices,the deposited data, batch data, and streamed data into spatial andtemporal factors to form segregated data; receiving, by the one or morecomputing devices, an analytic request for a forecast oftelecommunications services and network demand; conducting, by the oneor more computing devices, a multi-class analysis responsive to theanalytic request using the segregated data, wherein conducting themulti-class analysis comprises: generating vectors comprised of slopesfrom plural coefficients to determine demand elasticity from pluralfeatures; and generating, by the one or more computing devices and basedon the multi-class plural-factored elastic analysis, a real-timedemand-based forecast for the telecommunications services and networkdemand.
 2. The method of claim 1, wherein conducting the multi-classanalysis further comprises: computing a plurality of elasticitycoefficients for factors for a multi-class plural-factored elasticcluster (MPEC) analysis, obtaining multiple elasticity slope vectors,computing multiple clusters, embedding multi-class clusters within theelasticity slope vectors, and assigning output class bins.
 3. The methodof claim 1, further comprising: ingesting the streamed data in realtime.
 4. The method of claim 1, wherein the demand-based forecastincludes: a telecommunication product geographic demand forecast, atelecommunication product temporal demand forecast, or atelecommunication service deployment forecast.
 5. The method of claim 1,wherein the batch data or streamed data includes weather forecast data,and wherein generating the vectors further comprises using weatherforecast integrated elastic clusters.
 6. The method of claim 1, whereinthe batch data or streamed data includes social media data, and whereingenerating the vectors further comprises using social media integratedelastic clusters.
 7. The method of claim 1, wherein conducting themulti-class cluster analysis further comprises: converting at least someof the segregated data from classic bits to quantum bits.
 8. The methodof claim 7, wherein converting the segregated data from classic bits toquantum bits further comprises: conducting statistical symmetry andpattern evaluation of the segregated data, assigning a compressionprotocols based on the statistical symmetry and pattern evaluation,performing quantum data compression using the assigned compressionprotocol to generate compressed data, and assigning one of a qubiterasure protocol or a qubit non-erasure protocol to the compressed data.9. The method of claim 1, wherein conducting the multi-class clusteranalysis further comprises: identifying plural features, derivingindividual clusters from plots of the plural features, and computing acluster distance matrix for each of the clusters.
 10. A systemcomprising: one or more computing devices, comprising a communicationinterface, a memory that stores instructions, and a processor thatexecutes the instructions to: receive deposited data, batch data, andstreamed data that relates to telecommunications services andtelecommunication network components; segregate the deposited data,batch data, and streamed data into spatial and temporal factors to formsegregated data; receive an analytic request for a forecast oftelecommunications services and network demand; conduct a multi-classcluster analysis responsive to the analytic request using the segregateddata, wherein conducting the multi-class cluster analysis comprises:generating vectors comprised of slopes from plural coefficients todetermine demand elasticity from plural features; and generate, based onthe multi-class cluster analysis, a real-time demand-based forecast forthe telecommunications services and network demand.
 11. The system ofclaim 10, wherein, when conducting the multi-class cluster analysis, theprocessor further executes the instructions to: compute a plurality ofelasticity coefficients for factors for a multi-class plural-factoredelastic cluster (MPEC) analysis, obtain multiple elasticity slopevectors, compute multiple clusters, embed multi-class clusters into theelasticity slope vectors, and assign output class bins.
 12. The systemof claim 10, wherein, when receiving the streamed data, the processorfurther executes the instructions to: ingest the streamed data in realtime.
 13. The system of claim 10, wherein the demand-based forecastincludes: a telecommunication product geographic demand forecast,telecommunication product temporal demand forecast, or atelecommunication service deployment forecast.
 14. The system of claim10, wherein the batch data or streamed data includes weather forecastdata, and wherein, when generating the vectors, the processor furtherexecutes the instructions to use weather forecast integrated elasticclusters.
 15. The system of claim 10, wherein the batch data or streameddata includes social media data, and wherein, when generating thevectors, the processor further executes the instructions to use socialmedia integrated elastic clusters.
 16. The system of claim 10, wherein,when conducting the multi-class cluster analysis, the processor furtherexecutes the instructions to: convert at least some of the segregateddata from classic bits to quantum bits.
 17. A non-transitory,computer-readable storage medium storing instructions executable by aprocessor of a network element, which when executed cause the networkelement to: receive deposited data, batch data, and streamed data thatrelates to telecommunications services and telecommunication networkcomponents; segregate the deposited data, batch data, and streamed datainto spatial and temporal factors to form segregated data; receive ananalytic request for a forecast of telecommunications services andnetwork demand; conduct a multi-class plural-factored elastic cluster(MPEC) analysis responsive to the analytic request using the segregateddata, wherein conducting the MPEC analysis comprises: generating vectorscomprised of slopes from plural coefficients to determine demandelasticity from plural features; and generate, based on the MPECanalysis, a real-time demand-based forecast for the telecommunicationsservices and network demand.
 18. The non-transitory, computer-readablemedium of claim 17, wherein the instructions to conduct the MPECanalysis when executed further cause the network element to: obtainmultiple elasticity slope vectors, compute plural clusters, and embedmulti-class clusters within elasticity slope vectors.
 19. Thenon-transitory, computer-readable storage medium of claim 17, whereinthe instructions to conduct the MPEC analysis when executed furthercause the network element to: convert at least some of the segregateddata from classic bits to quantum bits, and assign a gate for quantumbit conversion based on a matrix structure of the segregated data. 20.The non-transitory, computer-readable storage medium of claim 17,wherein the instructions to conduct the MPEC analysis when executedfurther cause the network element to: identify plural features, deriveindividual clusters from plots of the plural features, and compute acluster distance matrix for each of the clusters.